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In each of the three risk-scoring systems tested (Figure 1, B to D, F to H, and J to L), the prognostic performance of the nested models and survival curves that contained GRG status along with the CD4 and viral load (i.e., C+V+G) were superior to those risk-scoring systems that excluded the GRGs (i.e., C+V).
Univariate and multivariate analyses were performed using log-rank and Cox proportional hazards models, and survival curves were estimated using the Kaplan-Meier method.
To identify the factors associated with patients' and caregivers' QoL, and caregivers' burden, linear regression models and survival models will be used.
The Wald and log-rank tests were used respectively for mixed models and survival analyses based on the TTD to test the rejection of the null hypothesis.
The link between management in CBUs and evolution of BPSD during patients' hospitalization will be assessed with linear regression models and survival models.
The association between changes in psychotropic drug consumption (especially neuroleptic drugs) and the evolution of BPSD will be assessed with linear regression models and survival models.
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Logit modeling and survival analysis are used to identify significant incentive- and capacity-related factors explaining behavior of firms toward initial adoption, speed of this adoption, continued certification, and the length of time between certification and decertification.
The optimal feature set for subtype classification model and survival prediction model were different, but the fore features were the same.
Clonogenic survival post IR was analysed using the linear quadratic model and survival post MMC treatment was analysed using a two components exponential model.
Data were analyzed using analysis of variance, multilevel modeling, and survival analyses, where appropriate.
Survival analyses were performed by using a Cox model, and survival curves were estimated with the Kaplan-Meier method.
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